Machine-aided geological interpretation provides an opportunity for rapid and data-driven decision-making. In disciplines such as geostatistics, the integration of machine learning has the potential to improve the reliability of mineral resources and ore reserve estimates. In this study, inspired by existing geostatistical approaches that use radial basis functions to delineate domain boundaries, we reformulate the problem into a machine learning task for automated domain boundary delineation to partition the orebody. We use an actual dataset from an operating mine (Driefontein gold mine, Witwatersrand Basin in South Africa) to showcase our new method. Using various machine learning algorithms, domain boundaries were created. We show that based on a combination of in-discipline requirements and heuristic reasoning, some algorithms/models may be more desirable than others, beyond merely cross-validation performance metrics. In particular, the support vector machine algorithm yielded simple (low boundary complexity) but geologically realistic and feasible domain boundaries. In addition to the empirical results, the support vector machine algorithm is also functionally the most resemblant of current approaches that makes use of radial basis functions. The delineated domains were subsequently used to demonstrate the effectiveness of domain delineation by comparing domain-based estimation versus non-domain-based estimation using an identical automated workflow. Analysis of estimation results indicate that domain-based estimation is more likely to result in better metal reconciliation as compared with non-domained based estimation. Through the adoption of the machine learning framework, we realized several benefits including: uncertainty quantification; domain boundary complexity tuning; automation; dynamic updates of models using new data; and simple integration with existing machine learning-based workflows.